Abstract: The present disclosure is directed to numerically estimating the shear modulus of Kerogen by using a combination of mineralogy from digital image analysis and sonic log analysis, when measured data on only one elastic constant (Bulk, Young's or P-wave modulus) is available. In some instances, elastic properties predicted from the digital images are compared with sonic, shear, and density logs, to estimate the shear modulus of kerogen. As a one-to-one correspondence is not expected between the core sub-samples and the rock unit sampled by the well logs, cross-property relations can be used to identify the suitability of the effective medium models and to iteratively determine the shear modulus of kerogen.

Abstract: The present disclosure is directed to numerically estimating the shear modulus of Kerogen by using a combination of mineralogy from digital image analysis and sonic log analysis, when measured data on only one elastic constant (Bulk, Young's or P-wave modulus) is available. In some instances, elastic properties predicted from the digital images are compared with sonic, shear, and density logs, to estimate the shear modulus of kerogen. As a one-to-one correspondence is not expected between the core sub-samples and the rock unit sampled by the well logs, cross-property relations can be used to identify the suitability of the effective medium models and to iteratively determine the shear modulus of kerogen.

Abstract: A method for updating a velocity model of a subsurface of the earth is described herein. A tomographic update to the velocity model of the subsurface may be performed to generate a tomographic velocity model update. A geomechanical velocity model update of the subsurface may be calculated. The geomechanical velocity model update may be combined with the tomographic velocity model update.

Abstract: A method and apparatus for constructing a velocity model of a subsurface of the earth, is disclosed herein. Seismic data may be received. A first velocity model of the subsurface may be constructed using the seismic data. The subsurface may have one or more salt bodies. A second velocity model of the subsurface without the salt bodies may be constructed using the seismic data. A set of attributes induced by the salt bodies may be determined based on the first velocity model and the second velocity model. A stiffness tensor change between the first velocity model and the second velocity model may be calculated based on the set of attributes induced by the salt bodies. The first velocity model may be updated based on the stiffness tensor change.

Abstract: Various implementations described herein are directed to estimating stresses and elastic parameters in a formation based on seismic data. In one implementation, wide azimuth seismic data may be used to derive anisotropic elastic parameters. Furthermore, stresses may be calculated using a geomechanical earth model, followed by deriving anisotropic elastic parameters based on the calculated stresses. The anisotropic elastic parameters derived from the wide azimuth seismic data may then be used to modify the geomechanical earth model to improve the prediction of drilling parameters.

Abstract: A technique includes inverting seismic data acquired for a subsurface region to determine dynamic elastic properties and converting the dynamic elastic properties to static elastic properties and rock strength properties. The technique includes generating a three-dimensional mechanical earth model for the subsurface region, where the model includes the dynamic elastic properties, the static elastic properties, the rock strength properties and a subsurface stress field. The technique can include a calibration step that matches seismic data observations to predictions from the geomechanical model.

Abstract: A method for updating a velocity model of a subsurface of the earth is described herein. A tomographic update to the velocity model of the subsurface may be performed to generate a tomographic velocity model update. A geomechanical velocity model update of the subsurface may be calculated. The geomechanical velocity model update may be combined with the tomographic velocity model update.

Abstract: A method and apparatus for constructing a velocity model of a subsurface of the earth, is disclosed herein. Seismic data may be received. A first velocity model of the subsurface may be constructed using the seismic data. The subsurface may have one or more salt bodies. A second velocity model of the subsurface without the salt bodies may be constructed using the seismic data. A set of attributes induced by the salt bodies may be determined based on the first velocity model and the second velocity model. A stiffness tensor change between the first velocity model and the second velocity model may be calculated based on the set of attributes induced by the salt bodies. The first velocity model may be updated based on the stiffness tensor change.

Abstract: A plurality of images, including a first image and a second image having a higher resolution than the first image, are aligned by generating an oversampled cross correlation image that corresponds to relative displacements of the first and second images, and, based on the oversampled cross correlation image, determining an offset value that corresponds to a misalignment of the first and second images. The first and second images are aligned to a precision greater than the resolution of the first image, based on the determined offset value. Enhanced results are achieved by performing another iteration of generating an oversampled cross correlation image and determining an offset value for the first and second images. Generating the oversampled cross correlation image may involve generating a cross correlation image that corresponds to relative displacements of the first and second images, and oversampling the cross correlation image to generate the oversampled cross correlation image.

Abstract: A plurality of images, including a first image and a second image having a higher resolution than the first image, are aligned by generating an oversampled cross correlation image that corresponds to relative displacements of the first and second images, and, based on the oversampled cross correlation image, determining an offset value that corresponds to a misalignment of the first and second images. The first and second images are aligned to a precision greater than the resolution of the first image, based on the determined offset value. Enhanced results are achieved by performing another iteration of generating an oversampled cross correlation image and determining an offset value for the first and second images. Generating the oversampled cross correlation image may involve generating a cross correlation image that corresponds to relative displacements of the first and second images, and oversampling the cross correlation image to generate the oversampled cross correlation image.

Abstract: Various implementations described herein are directed to estimating stresses and elastic parameters in a formation based on seismic data. In one implementation, wide azimuth seismic data may be used to derive anisotropic elastic parameters. Furthermore, stresses may be calculated using a geomechanical earth model, followed by deriving anisotropic elastic parameters based on the calculated stresses. The anisotropic elastic parameters derived from the wide azimuth seismic data may then be used to modify the geomechanical earth model to improve the prediction of drilling parameters.

Abstract: A charged particle beam system and scanning control method capable of imaging, and possibly editing, a device under test (DUT). The charged particle beam system contains a charged particle beam generation unit, such as a focused ion beam (FIB) column, which emits a charged particle beam onto the DUT. Also included is a scan controller arrangement implementing a finite state machine to control the application of the charged particle beam onto the DUT according to a plurality of scanning control parameters. The scanning control parameters may describe one or more scan regions that are rectangular in shape. Further, the parameters may describe one or more scan regions describing other shapes by way of a bit-map. Similarly, a method for controlling the scanning of a charged particle beam that involves obtaining a set of scanning control parameters, and then directing the charged particle beam as specified by the scanning control parameters by way of a finite state machine, is disclosed.

Abstract: A charged particle beam system and scanning control method capable of imaging, and possibly editing, a device under test (DUT). The charged particle beam system contains a charged particle beam generation unit, such as a focused ion beam (FIB) column, which emits a charged particle beam onto the DUT. Also included is a scan controller arrangement implementing a finite state machine to control the application of the charged particle beam onto the DUT according to a plurality of scanning control parameters. The scanning control parameters may describe one or more scan regions that are rectangular in shape. Further, the parameters may describe one or more scan regions describing other shapes by way of a bit-map. Similarly, a method for controlling the scanning of a charged particle beam that involves obtaining a set of scanning control parameters, and then directing the charged particle beam as specified by the scanning control parameters by way of a finite state machine, is disclosed.

Abstract: A plurality of images, including a first image and a second image having a higher resolution than the first image, are aligned by generating an oversampled cross correlation image that corresponds to relative displacements of the first and second images, and, based on the oversampled cross correlation image, determining an offset value that corresponds to a misalignment of the first and second images. The first and second images are aligned to a precision greater than the resolution of the first image, based on the determined offset value. Enhanced results are achieved by performing another iteration of generating an oversampled cross correlation image and determining an offset value for the first and second images. Generating the oversampled cross correlation image may involve generating a cross correlation image that corresponds to relative displacements of the first and second images, and oversampling the cross correlation image to generate the oversampled cross correlation image.

Abstract: A plurality of images, including a first image and a second image having a higher resolution than the first image, are aligned by generating an oversampled cross correlation image that corresponds to relative displacements of the first and second images, and, based on the oversampled cross correlation image, determining an offset value that corresponds to a misalignment of the first and second images. The first and second images are aligned to a precision greater than the resolution of the first image, based on the determined offset value. Enhanced results are achieved by performing another iteration of generating an oversampled cross correlation image and determining an offset value for the first and second images. Generating the oversampled cross correlation image may involve generating a cross correlation image that corresponds to relative displacements of the first and second images, and oversampling the cross correlation image to generate the oversampled cross correlation image.

Abstract: A plurality of images, including a first image and a second image having a higher resolution than the first image, are aligned by generating an oversampled cross correlation image that corresponds to relative displacements of the first and second images, and, based on the oversampled cross correlation image, determining an offset value that corresponds to a misalignment of the first and second images. The first and second images are aligned to a precision greater than the resolution of the first image, based on the determined offset value. Enhanced results are achieved by performing another iteration of generating an oversampled cross correlation image and determining an offset value for the first and second images. Generating the oversampled cross correlation image may involve generating a cross correlation image that corresponds to relative displacements of the first and second images, and oversampling the cross correlation image to generate the oversampled cross correlation image.